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Problem definition: We study a robust screening problem where a seller attempts to sell a product to a buyer knowing only the moment and support information of the buyer's valuation distribution. The objective is to maximize the competitive ratio relative to an optimal hindsight policy equipped with full valuation information. Methodology/results: We formulate the robust screening problem as a linear programming problem, which can be solved efficiently if the support of the buyer's valuation is finite. When the support of the buyer's valuation is continuous and the seller knows the mean and the upper and lower bounds of the support for the buyer's valuation, we show that the optimal payment is a piecewise polynomial function of the valuation with a degree of at most two. Moreover, we derive the closed-form competitive ratio corresponding to the optimal mechanism. The optimal mechanism can be implemented by a randomized pricing mechanism whose price density function is a piecewise inverse function adjusted by a constant. When the mean and variance are known to the seller, we propose a feasible piecewise polynomial approximation of the optimal payment function with a degree of at most three. We also demonstrate that the optimal competitive ratio exhibits a logarithmic decay with respect to the coefficient of variation of the buyer's valuation distribution. Managerial implications: Our general framework provides an approach to investigating the value of moment information in the robust screening problem. We establish that even a loose upper bound of support or a large variance can guarantee a good competitive ratio. Funding: The research of S. Wang is partially supported by the National Natural Science Foundation of China [Grant 72394395]. The research of S. Liu is partly supported by the National Natural Science Foundation of China [Grant NSFC-72072117]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0072. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) is considered one of the main pillars for transforming the conventional "dark" energy system to a net-zero carbon or "green" energy system. This work reviewed the potential resources for producing low-carbon hydrogen in China, as well as the possible hydrogen production methods based on the available resources. The analysis and comparison of the levelized cost of hydrogen (LCOH) for different hydrogen production pathways, and the optimal technology mixes to produce H2 in China from 2020 to 2050 were obtained using the mixed-integer linear programming (MILP) optimization model. The results were concluded as three major ones: (a) By 2050, the LCOH of solar- and onshore-wind-powered hydrogen will reach around 70–80 $/MWh, which is lower than the current H2 price and the future low-carbon H2 price. (b) Fuel costs (>40%) and capital investments (~20%) of different hydrogen technologies are the major cost components, and also are the major direction to further reduce the hydrogen price. (c) For the optimal hydrogen technology mix under the higher renewable ratio (70%) in 2050, the installed capacities of the renewable-powered electrolysers are all more than 200 GW, and the overall LCOH is 68.46 $/MWh. This value is higher than the LCOH (62.95 $/MWh) of the scenario with higher coal gasification with carbon capture and the storage (CG-CCS) ratio (>50%). Overall, this work is the first time that hydrogen production methods in China has been discussed comprehensively, as well as the acquisition of the optimal H2 production technology mix by the MILP optimization model, which can provide guidance on future hydrogen development pathways and technology development potential in China. [ABSTRACT FROM AUTHOR]

Organization's sustainability performance is influenced by its suppliers' sustainability performance. This relationship makes sustainable supplier development a strategic competitive option for a buyer or focal organization. When considering sustainable supplier development practices (SSDPs) adoption, organizations have to balance and consider their limited financial resources and operational constraints. It becomes necessary to both select the best SSDPs set and investment allocation among the selected SSDP set such that the organization can maximize overall sustainability performance level. In this paper, an integrated formal modeling methodology using DEMATEL, the NK model, and multi-objective linear programming model is used support this objective. The proposed methodology is evaluated in a practical sustainable supply chain field study of an equipment manufacturing company in China. Through case study, we found that the interdependency among SSDPs must be considered in SSDPs selection and investment allocation problem. Theoretical, managerial and methodology implications, conclusions, and directions for future research are also presented. [ABSTRACT FROM AUTHOR]

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities. [ABSTRACT FROM AUTHOR]

Reverse logistics networks reintroduce end-of-life products to remanufacturing, which is significant for sustainable development and environmental protection. In this paper, we investigate the current reverse logistics network of household appliances in China and redesign a new network that introduces necessary facilities: disassembly centres and three types of remanufacturing plants to improve recycling rates. A mixed-integer linear programming model with multi-level capacity choices is proposed to determine the locations and capacities for disassembly centres and remanufacturing plants. An efficient decomposition-and-expansion heuristic is developed to solve the model. The redesigned network is evaluated using a real case. The results indicate that the recycling rate is largely improved by the new network in which almost all useful materials can be recycled, whereas, in the traditional network, only metals are efficiently recycled. A sensitivity analysis of model parameters is conducted using the Taguchi method to identify their effects on recycling decisions. The proposed algorithm is further evaluated using a set of randomly generated instances. The results show that the algorithm can yield high-quality solutions within a short time. The best configuration of the algorithm is suggested via sensitivity analysis of parameters using the generated instances. [ABSTRACT FROM AUTHOR]

The Location-Routing Problem (LRP) becomes a more intricate subject when the limits of capacities of vehicles and warehouses are considered, which is an NP-hard problem. Moreover, as the number of vehicles increases, the solution to LRP is exacerbated because of the complexity of transportation and the combination of routes. To solve the problem, this paper proposed a Discrete Assembly Combination-Delivery (DACA) strategy based on, the Binary Equilibrium Optimizer (BiEO) algorithm, in addition, this paper also proposes a mixed-integer linear programming model for the problem of this paper. Our primary objective is to address both the route optimization problem and the assembly group sum problem concurrently. Our BiEO algorithm was designed as discrete in decision space to meet the requirements of the LRP represented by the DACA strategy catering to the multi-vehicle LRP scenario. The efficacy of the BiEO algorithm with the DACA strategy is demonstrated. through empirical analysis utilizing authentic data from Changchun City, China, Remarkably, the experiments reveal that the BiEO algorithm outperforms conventional methods, specifically GA, PSO, and DE algorithms, resulting in reduced costs. Notably, the results show the DACA strategy enables the simultaneous optimization of the LRP and the vehicle routing problem (VRP), ultimately leading to cost reduction. This innovative algorithm proficiently tackles both the assembly group sum and route optimization problems intrinsic to multi-level LRP instances. [ABSTRACT FROM AUTHOR]

Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call the traveling salesman problem with bike and robot (TSPBR), where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting. [ABSTRACT FROM AUTHOR]

Cainiao collaborates with thousands of logistics partners to provide the most cost- and time-efficient delivery services. In this work, we develop a decision support system to select good fulfillment routes consisting of partners for newly created parcels. The system's core optimization component contains a proportion algorithm based on solving an offline integer programming problem and an online algorithm based on dual values of the associated linear programming relaxation. The company has implemented the system to transport numerous parcels from China to other countries and regions, thus saving millions of dollars annually. [ABSTRACT FROM AUTHOR]

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters. [ABSTRACT FROM AUTHOR]

While solving the shunting scheduling of EMU first-level maintenance (SSEFM), most existing literature assumed a single maintenance route for all trains and considered only a through depot. It neglects the problem-specific characteristics in terms of varied maintenance routes and a stub-end depot, causing the infeasibility of the generated schedule in such particular circumstances. Therefore, the SSEFM problem with flexible maintenance routes in a stub-end depot with a transversal yard configuration is considered in this work. First, a multi-objective mixed-integer linear programming (MILP) model is formulated to maximize the reservation time in the storage area, and minimize the overstay time in the cleaning and inspecting areas. The relationship between constraints including flexible maintenance routes, train shunting conflicts, track occupation conflicts, and train arrival/departure times, are coordinated. Subsequently, a heuristic-based enhanced particle swarm optimization algorithm (EPSO) with two improvements is proposed to tackle this NP-hard problem. Specifically, three heuristic rules about the depth-first operation track allocation, the conflict-free bottleneck track allocation, and the right-shift track occupancy repair are designed to ensure the feasibility of the shunting schedule. Accordingly, a three-level decoding mechanism is designed to achieve a near-optimal shunting schedule with great train and route sequences. Two improvements on crossover and mutation operators are developed to enhance the exploration and exploitation ability. Finally, a real-world instance in China is solved to verify the effectiveness and efficiency of the proposed model and algorithm. Experimental results show that EPSO is relatively more effective than all the compared algorithms. [ABSTRACT FROM AUTHOR]

Typically understood through a universal-statist framework, modern schooling in contemporary China often contributes to the disenchantment of ethnic students. Based on year-long research in a Tibetan-serving secondary school, we provide additional insight in this discussion. We argue that to treat disenchantment as a fixed state ignores the space-temporal quality of human action. The school's social process is multiple and momentary in nature and often undermines the seemingly linear educational programming. Under the rigid school setting emerge social spaces that expand beyond academic lessons. In those spaces, students continue to interact among themselves and with teachers, where withdrawal and marginalization happen alongside negotiation, appropriation, and participation. While disenchantment anchors the classroom experience of many, it interpenetrates and enmeshes with other aspects of student lives and is interwoven over time. By considering this complex interplay of disenchantment we upend the notion of disenchantment as a singular state, and illustrate this idea through two examples. [ABSTRACT FROM AUTHOR]

• The influence of mileage fluctuations on maintenance scheduling is discussed. • The time windows with daily mileage fluctuations are constructed. • A 0–1 programming model for high-level maintenance scheduling is formulated. • An algorithm to adjust maintenance dates and planned daily mileages is proposed. • A real-world case study and a sensitivity analysis are conducted. The high-level maintenance plan is the train-set overhaul arrangement that is scheduled annually and manually in China. Previous studies have investigated this issue based on deterministic daily mileages. However, future daily mileages are difficult to predict, which may cause existing plans to be inaccurate and unfeasible in practice. Therefore, this study only considers the historical daily mileages as raw inputs, and the future daily mileages are considered as ranges to generate wider maintenance time windows for train-sets. Subsequently, a 0–1 integer linear programming model for high-level maintenance scheduling is formulated. After obtaining a high-level maintenance plan, the planned daily mileages of all train-sets are calculated and verified. To make all planned daily mileages feasible, we design an iterative algorithm to adjust the time windows and update the plan. A real-world case study is conducted using the data of 124 CRH2 EMU train-sets belonging to China Railway Shanghai Group to prove the effectiveness of the model and the algorithm. The commercial solver Gurobi is used to solve this case. A program supporting high-level maintenance scheduling has been developed, and it has been used by planners for testing. [ABSTRACT FROM AUTHOR]

The most important factors to consider in transportation are the items being transported, the origin and destination locations, the length of the trip, the mode of transportation required (truck, train, plane, or ship), the shipping due dates, the costs, the level of security, regulation, and monitoring, and the costs. Specifically, the goal of this study is to determine the cost of, delivery time for, and greenhouse gas (GHG) emissions related to the import of less than container load (LCL) from China to Pakistan. For the multiobjective problem, we employed the mixed-integer linear programming (MILP) approach to construct it while keeping the supply and demand constraints in mind. We tackled the problem using a multiobjective evolutionary algorithm and developed Pareto optimal solutions that could trade-off cost, time, and GHG emission in order to achieve the best possible result. According to the data, which was received from one of Pakistan's top logistics service companies, using intermodal transportation to carry a ten-tonne product from Shanghai in China to Kasur in Pakistan will cost 37 per cent more, take 14 per cent longer, and emit more greenhouse gases than using traditional multimodal transportation. [ABSTRACT FROM AUTHOR]

The most difficult problem for the control of heavy-haul trains is the cyclic air braking on long steep downward slopes. Unsuitable positions of the application of air braking will cause large longitudinal impulses, overspeeding and even accidents. This article proposes a mixed-integer linear programming (MILP) approach to the optimization of the speed curve of heavy-haul trains. First, with the objectives of maximizing the operation distance and minimizing the air braking time within a given period, an optimization model of a heavy-haul train is formulated considering multiple practical constraints, such as the speed limit, release time and releasing speed. Second, the optimization problem is transformed into an MILP problem by linearizing the nonlinear conditions, and is solved with existing CPLEX ® solvers. Finally, some case studies are conducted with actual data from the Shuohuang railway in China to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an uncertain interval stochastic fuzzy chance constraint land use optimal allocation method is proposed and applied to solve the problem of land use planning in river basins. The UISFCL-LUP method is an aggregation of interval parametric programming, fuzzy linear programming and chance constraint programming which can cope with uncertain problems such as interval value, fuzzy set and probability. In this paper, the uncertain mathematical method is explored and studied in the optimal allocation of land use in the next two planning periods of Nansihu Lake Basin in China. Moreover, it was proved that ISFCL-LUP can deal with the uncertainty of interval, membership function and probability representation and can also be used to solve the land use planning and land use strategy analysis under uncertain conditions. On the basis of model calculations, we obtained the optimal allocation results for six types of land use in four regions over two planning periods based on different environmental constraints. The results show that the optimized λ value (that is, the degree of satisfaction with all the model conditions) is in the range of [0.54, 0.79] and the corresponding system benefits are between [18.4, 20.4] × 1012 RMB and [96.7, 109.3] × 1012 RMB. The results indicate that land managers can make judgments based on the different socio-economic development needs of different regions and determine strategic land use allocation plans under uncertain conditions. At the same time, the model obtained interval solutions under different system satisfaction and constraint violation probabilities, which helps land managers to analyze the importance of land system optimization and sustainable development more deeply. [ABSTRACT FROM AUTHOR]

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic. [ABSTRACT FROM AUTHOR]

Giant hydrounits are usually accompanied by multiple head-dependent forbidden zones (FZs). FZs intensify hydropower systems' head effects and affect the safe and economic operations in a deregulated market environment, especially in southwest China. This paper addresses the self-scheduling and bidding strategies of a typical hydroproducer (the Longtan hydroplant, the leading plant on the Hongshuihe River, one of the most important plants in the West-East Electricity Transmission Project in China) with irregular forbidden zones (IFZs) in a day-ahead market. Considering the impacts of crossing IFZs and the head effects, a robust optimization-based approach to maximize profits was developed to establish market-oriented strategies under asymmetric price intervals and is solved by an iterative algorithm.The complicated model with nonconvex and nonlinear features was recast into a robust mixed-integer linear programming (MILP) model. Finally, the optimal self-scheduling of a hydroproducer with IFZs in southwest China was used to show the effectiveness of the proposed model. The optimization results demonstrated that the proposed model can effectively incorporate the head effects and the impacts of crossing IFZs to maximize profits; furthermore, the model significantly reduced the phenomenon of crossing IFZs and provided more executable, stable, and safe generation scheduling. Additionally, the proposed model can obtain more profitable optimal bidding strategies than the model that ignores the impacts of crossing IFZs. [ABSTRACT FROM AUTHOR]

The iron and steel industry has made an important contribution to China's economic development, and sinter accounts for 70–80% of the blast furnace feed charge. However, the average grade of domestic iron ore is low, and imported iron ore is easily affected by transportation and price. The intelligent ore blending model with an intelligent algorithm as the core is studied. It has a decisive influence on the development of China's steel industry. This paper first analyzes the current situation of iron ore resources, the theory of sintering ore blending, and the difficulties faced by sintering ore blending. Then, the research status of the neural network algorithms, genetic algorithms, and particle swarm optimization algorithms in the intelligent ore blending model is analyzed. On the basis of the neural network algorithm, genetic algorithm and particle swarm algorithm, linear programming method, stepwise regression analysis method, and partial differential equation are adopted. It can optimize the algorithm and make the model achieve better results, but it is difficult to adapt to the current complex situation of sintering ore blending. From the sintering mechanism, sintering foundation characteristics, liquid phase formation capacity of the sinter, and the influencing factors of sinter quality were studied, it can carry out intelligent ore blending more accurately and efficiently. Finally, the research of intelligent sintering ore blending model has been prospected. On the basis of sintering mechanism research, combined with an improved intelligent algorithm. An intelligent ore blending model with raw material parameters, equipment parameters, and operating parameters as input and physical and metallurgical properties of the sinter as output is proposed. [ABSTRACT FROM AUTHOR]

Service network design is a typical tactical planning problem faced in freight railway transportation. In this paper, we propose an integer linear programming model for the rail freight service network design problem in the context of the China railway system. The model aims to minimize the overall costs (including train costs and car costs) while satisfying a set of practical restrictions and operational requirements. Our model also considers a distinct operational feature in Chinese railway transportation practice, i.e., the tree-shaped path. A numerical experiment as well as a real-life case study from the China Railway Jinan Group (CR Jinan) is conducted to demonstrate the proposed solution approach. The real-life problem size reaches as many as 139 stations, 473 alternative train services and 562 shipments. Computational results show that our proposed approach is able to obtain quality solutions within a reasonable time frame. [ABSTRACT FROM AUTHOR]

Illegal mining activities on offshore sands have recently increased dramatically in China owing to the strong demand of the construction industry and restriction of river sand mining. By studying the loss mechanism of marine sand resources, this study proposes a simplified linear programming model to quantitatively analyse the control effects related to resource loss and law enforcement costs. To accurately simulate the effectiveness of law enforcement activity, a set of variables is designed in the model to describe the severity of illegal mining and enforcement skills of the officers. Subsequently, based on different settings of law enforcement capability, the model can not only make reasonable projections for sand losses but also provide a minimum cost solution for marine sand control for government agencies. Finally, various regulatory measurements and suggestions are discussed to improve the effectiveness of the government's regularisation and cost optimisation for law enforcement. [ABSTRACT FROM AUTHOR]

The joint operation mode of large hydropower plants and their reverse-regulating plants has been widely used in China's major river basins. Hence this paper establishes an optimization model for the short-term optimal scheduling of cascade hydropower plants with reverse-regulating effects. Two conflicting objectives that need to be considered for optimal operation are minimizing the peak-valley difference of the power grid and minimizing the outflow variation of the reverse-regulating plant. The complex hydraulic coupling between the main-regulating plant and reverse-regulating plant is of particular concern. A novel two-layer nested approach, coupling constraint method and a mixed-integer linear programming (MILP) approach, is therefore proposed to solve the model. The results for three case studies demonstrate that: 1) The proposed approach is computationally efficient, and the average time for a single calculation is about 12 min; 2) The peak-valley difference of power grid G in dry season and flood season is reduced by 26.0% and 15.7%, respectively, after optimization. The outflow variation of the reverse-regulating plant has been controlled at 300 and 350 m3/s, respectively. 3) The developed model produces a more realistic and executable scheduling scheme than the benchmark model which does not consider the complex hydraulic coupling between cascade plants. [Display omitted] [ABSTRACT FROM AUTHOR]

Power systems face adequacy risks because of the high integration of renewable energy. It is urgent to develop efficient methods for power system operational reliability assessment. Conventional power system reliability assessment methods cannot achieve real-time assessment of system risk because of the high computational complexity and long calculation time. The high computational complexity is mainly caused by a large number of optimal power flow (OPF) calculations. To reduce the computational complexity, this paper transfers the optimal power flow model as a multiparameter linear programming model. Then, the optimal power flow can be obtained by linear calculations. Furthermore, this paper proposes a state reduction method considering the importance index of transmission lines for further improving the calculation efficiency. Case studies are carried out on IEEE standard systems and a provincial power grid in China. Compared with the conventional reliability assessment method, the reliability assessment efficiency of the proposed method increases by 10–40 times, and the assessment error is less than 1%. [ABSTRACT FROM AUTHOR]

An oasis is a unique landscape that fuels human subsistence and socioeconomic development in the desert ecosystem. However, the overexpansion of oases, especially farmlands, poses severe threats to available land and water resources. This study aims to assess the expansion levels, carrying capacity dynamics, and planting structure optimization to maximize economic returns in northwest China's five Typical Oasis Groups (5TOGs) using uniform data sources, time scales, and methods. Satellite products and a water-heat balance model were used to evaluate the changes in the area and carrying capacity dynamics of the 5TOGs. A linear programming approach was used to optimize each oasis's cropping structure for the carrying level scenario. The results showed that the area of 5TOGs has expanded from 1980–2020, and the increment of oasis farmland is the main driver of oasis expansion. The most dramatic expansion of oases and their farmlands occurred during 2010–2020. As a consequence, the carrying capacity of each oasis is deteriorating with this expansion. The additional water resources to support this expansion of the oases and their farmlands come from groundwater, which is declining rapidly. Based on the optimized planting structure, cotton remains the main crop in Xinjiang oases with more than 60% area, the cotton area should be reduced in the Hotan River Oases, and the planting structure of the Heihe River Oasis will remain unchanged. The findings of this study have provided a quantitative analysis of oasis expansion and planting structure optimization, which have practical implications for water resource management and sustainable development of agriculture to maintain the stability of the oasis ecosystem. [ABSTRACT FROM AUTHOR]

The intercity railway is subject to variation and fluctuation in demand both in time and space over a day to a large extent. In that case, more advanced line planning techniques are practically needed to match the non-equilibrium passenger demand. We propose an integer linear programming model for adapting to the fluctuating demand and improving rail line profit, in which the multi-period planning approach and flexible train composition mode are taken into consideration. In particular, we also consider the limitations of the carriage and the dynamic transfer of resources during a finite period to ensure the better implementation of the line planning and subsequent operation plans. Our purpose is to improve on previous line planning models by integrating the multi-period strategic-level line planning decision with resource constraints. Since the problem is computationally intractable for realistic size instances, an improved round heuristic algorithm that is based on linear relaxation is proposed and we compare the round heuristic performance with the commercial solver Gurobi on artificial instances. The numerical experiments that are based on an intercity railway in China certify the effectiveness and applicability of the proposed model and algorithm. We evaluate the impact of different optimization parameters and reserved carriages and the computation results show that in comparison to the fixed composition mode, the proposed approach can improve the utilization efficiency of carriage resources and increase the line profit by 1.9% under the same carriage resource conditions. [ABSTRACT FROM AUTHOR]

A portfolio selection model with return as triangular intuitionistic fuzzy number is developed in this study to assess and select portfolios on China Stock Exchange. Although the portfolio selection has been widely investigated, and most studies have regarded return and risk as the main decisive criteria, there are many uncertainties in the financial market, such as social, political and human psychological factors, which makes it difficult for us to describe the risks in line with empirical evidence. To fill this gap, first, a literature review was conducted to clarify the current situation and shortcomings of portfolio selection. Second, the triangular intuitionistic fuzzy number was used to fuzzify the coefficients of the objective function and the constraints in the portfolio model. Third, the triangular intuitionistic fuzzy number model was transformed into a linear programming model by using the selected exact ranking function, and the model was solved by MATLAB. Finally, the historical monthly returns of 10 stocks in China's Stock Exchange from December 2017 to November 2020, lasting 36 months, were selected to demonstrate the model. The results indicate that the portfolio selection model with triangular intuitionistic fuzzy number can better meet the uncertainty of the real securities market, and more reasonable investment decision guidance can be provided for investors. Besides, the limitations of this study are pointed out, and implications and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

With the gradual opening of China's electricity market, it is effective for cascade hydropower plants to simultaneously participate in both the monthly contract market and the day-ahead spot market to obtain higher power generation benefits. Hence, this paper studies the optimal decomposition model for the monthly contracted electricity of cascade hydropower plants considering the bidding space in the day-ahead spot market. The close hydraulic and electric connection between cascade hydropower plants, the implementation requirements of contracted electricity, and the uncertainty of the day-ahead market clearing price are all well considered. Several linearization techniques are proposed to address the nonlinear factors, including the objective function and the power generation function. A successive approximation (SA) approach, along with a mixed-integer linear programming (MILP) approach, is then developed to solve the proposed model. The presented model is verified by taking the decomposition of the monthly contracted electricity of cascade hydropower plants in China as an example. The results indicate that the developed model has high computational efficiency and can increase the power generation benefits compared with the conventional deterministic model. The effect of the penalty coefficient for imbalanced monthly contracted electricity is also evaluated, which provides a practical reference for market managers. [ABSTRACT FROM AUTHOR]

Purpose: Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets. Design/methodology/approach: First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model. Findings: The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods. Originality/value: The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes. [ABSTRACT FROM AUTHOR]

The increasing prevalence of electric vehicles (EVs) will lead to a continuous rise in the quantity of waste batteries in the future. The topic regarding reverse logistics network design (RLND) for waste batteries has garnered extensive attention from both the academic and societal realms. However, relevant research in this area has yet to delve deeper. This research proposes a mixed-integer linear programming (MILP) model to optimize the multi-level reverse logistics network (RLN) of waste batteries from the perspective of a circular economy. The model aims to minimize costs as an economic objective and to reduce carbon emissions as an environmental objective. It introduces an expected weighting factor to balance these two conflicting goals. Various technological levels are considered for different processing facilities, thereby enhancing the sustainability of the RLN. The model is applied to a numerical case study in the Nanjing Metropolitan Area of China, obtaining optimal network facility location schemes, technological configuration plans, and transportation schemes between facilities. The study emphasizes the significance of selecting different technology types for facilities, as rational process configurations effectively reduce recycling operational costs and decrease network carbon emissions. Sensitivity analysis emphasizes the impact of uncertain factors on RLND, highlighting the imperative need for a forward-thinking approach to ensure long-term sustainable development for enterprises. Managerial insights aim to guide enterprises and government departments, encouraging the adoption of higher technology types to achieve the dual goals of increased revenue and reduced carbon emissions. • A multi-level reverse logistics network suitable for China has been designed. • Reasonable utilization of batteries with varying performance levels. • Consideration of diverse green technologies for enhanced network sustainability. • Reasonable tech configuration reduces costs and carbon emissions effectively. • Encouraging forward-looking network design with advanced technology adoption. [ABSTRACT FROM AUTHOR]

We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers' travel and reduces the cost of train operation for a rail transit network. The model is an integer non linear programming model that aims to minimize the entrance and platform waiting time of passengers and the operation cost of trains. The timetable and variable train formation are integrated optimized, and the turnaround of rolling stock is also considered by constructing the train operation constraints for the model. The coordination of timetables on different lines and the coordination of passenger control strategies at different stations are mentioned by considering the transfer passengers when constructing the passenger control constraints. To solve the model, a multi-operator simulated annealing (MOSA) algorithm that includes three types of execution operators corresponding to three main decision variables is proposed. A numerical case that includes 2 bidirectional crossed lines and a real-world case from China are introduced to test the efficiency of the proposed method, which demonstrates better performance than the single and respective optimization solutions. [ABSTRACT FROM AUTHOR]

With the implementation of China's rural revitalization strategy, the social economy of villages is expected to fully develop; however, their carbon emissions must be controlled within a reasonable range. Realization of this goal is part of the guidance and control of village planning. Clarifying the coupling relationship between village land uses and rural carbon emissions is fundamental for low-carbon village planning. In this study, by exploring the relationships between carbon emissions factors, land-use types, and human activities, the reference range of carbon emissions coefficients for various land-use types in rural areas is obtained. Then, based on the interval values of carbon emissions coefficients, a two-stage optimization model for village carbon emissions analysis is established, which is used to generate the minimal value of village carbon emissions and planning schemes to achieve different carbon emissions target values. First, the smallest carbon emissions value for a certain village is obtained based on a linear programming model. Then, to analyze the planning scheme possibilities under different carbon emissions targets, an objective planning model (including various parameters) is constructed. Through this two-stage optimization model, the optimal planning scheme is set and corresponding planning indicators under different scenarios are obtained through a sensitivity analysis. Combined with a case study in Dongzhuang Village, Shanghai, the results indicate that, with continuous improvement of the basic national carbon emissions database, the range of carbon emissions coefficients for typical local land uses can be determined, and the carbon emissions and land-use types of villages can be co-planned using the two-stage optimization model. With the proposed model, the range of carbon emissions for villages and scenario analysis results considering carbon emissions values associated with various land-use planning schemes can be obtained, contributing greatly to low-carbon village planning. [ABSTRACT FROM AUTHOR]

In high-speed rail networks, train units are scheduled to periodically meet all maintenance requirements while at the same time continuing to serve all scheduled passenger trips. Motivated by the trip demand variances on the days of every week in China, this paper studies a weekly rolling stock planning (W-RSP) problem that aims to optimize the rotation plan for the train units on each day of a week, so as to minimize their operating cost, including any (un)coupling costs and maintenance costs. We model the W-RSP on a newly developed rotation network by adopting particular nodes and arcs to address the (un)coupling operations of train units, and then propose an integer linear programming formulation for the problem. To solve this formulation, we develop a customized branch-and-price algorithm, which relies on a reduced linear programming relaxation for computing the lower bound, embeds a diving algorithm for computing the upper bound, and integrates advanced branching rules for effective explorations of the solution space. Computational results validate the effectiveness and efficiency of the proposed solution algorithm, which is able to solve large instances with up to 5034 trips to near-optimality. • A weekly rolling stock problem integrating rolling stock rotation and maintenance routing decisions. • A set-covering-like integer programming formulation based on a newly developed weekly rotation network. • A customized branch-and-price algorithm to solve the weekly rolling stock problem. • Conduct extensive numerical experiments to validate the effectiveness and efficiency of the solution algorithm. [ABSTRACT FROM AUTHOR]

Gravity measurement is an important geophysical prospecting method for mineral exploration. With most of the shallow ore deposits in China being exploited, the future targets for exploration will be aimed at the deep mineral resources about 4-km underground. To improve the resolving ability in the vertical direction of the 3-D density inversion to find deep-source minerals, we propose to achieve the inversion of gravity anomalies by combining power-spectrum derived adaptive weighting and cross-gradient regularization. First, spectral analysis is utilized to conduct the source-depth separation, and the adaptive weighting function is designed depending on the slope of the radial logarithmic power spectrum of the gravity anomaly, which can enhance the correspondence for the field sources with different depths to improve the vertical resolution relatively. Thus, each separated source with different depths could be given an adaptive weighting coefficient in each separated inversion. Second, the cross-gradient technique is introduced as a structural constraint in the objective function to further constrain the separated inversion process. By performing two-cuboid-source cases, it can be seen that this combination calculated approach has great potential to improve the quality of the inversion results. When examining the inversion of the actual gravity data from certain ore district in the west of Zhen’an, South Qinling, we speculate that a potential deep ore body would exist in the deposit via utilizing the recovered density model obtained by the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is devoted to the management of advance admission requests for obstetric care. Pregnant women in China select one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy. Schedulers must make the admission decision instantly based on the availability of the most critical resource, that is, hospital beds for postnatal care. The random delay between admission requests and postnatal care has created a distinct advance admission control problem. To address this issue, we propose a basic model that assumes a unit bed requirement for one day. Each admission generates a unit of revenue and each unit of overcapacity use incurs an overcapacity cost. With the objective of maximizing the expected net revenue, we establish an optimal policy for unlimited requests, that is, an expected arrival time quota (EATQ) policy that accepts a fixed quota of advance admission requests with the same expected date of confinement. We then propose an extended model for general capacity requirements. Using the Poisson approximation, we establish the optimality of the EATQ policy, which is shown to be solvable by a simple linear programming model. We compare the numerical results from the different policies and conduct a sensitivity analysis. The EATQ policy is demonstrated to be the best option in all test instances and notably outperforms the current admission rules used in hospitals, which usually accept admission requests according to some empirical monthly quota of the expected delivery month. The Poisson approximation is shown to be effective for determining the optimal EATQ policy for both stationary and nonstationary arrivals. Summary of Contribution: First, this paper investigates the advance admission control problem for obstetric care. Pregnant women in China choose one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy but the most critical resource is hospitalization beds needed for postnatal care. The random delay between admission request and postnatal care makes the problem unique and challenging to solve. It belongs to the scope of computing and operations research. Second, this paper formulates a dynamic programming model, analyzes the structural properties of the optimal control policy, and finally proposes a mathematical programming model to determine the optimal quota. Numerical experiments show the validity of the proposed approach. It covers the research contents of theories on dynamic stochastic control, mathematic programming model, and experiments. Moreover, this paper is motivated by the practical problem (advance admission control) in obstetric units of Shanghai. Using these optimality properties, solution approaches, and numerical results, this paper provides guidance on how to manage advance obstetric admission requests. [ABSTRACT FROM AUTHOR]

The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity–gas–heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency. [ABSTRACT FROM AUTHOR]

Because delivery systems play an increasingly significant role in modern life, it is of strategical importance to design an efficient and adaptive delivery network. JD.com is the largest business-to-consumer e-commerce company by revenue in China and has built the largest amount of logistics infrastructure nationwide. In this study, we developed an optimization model to automatically make annual plans of the delivery stations that minimize operational costs. We built an optimization algorithm by adopting a multiperiod facility location model a using mixed-integer linear programming (MILP) method and proposing applicable techniques to solve the model. The results demonstrate that our model generates improved plans compared with manually planning in terms of both operational cost and delivery distance. The tool described in this paper helps JD.com save on delivery costs and gain operational benefits in designing delivery networks. [ABSTRACT FROM AUTHOR]

In this study, we study a hybrid RDC‐DC serial inventory system where the regional distribution center (RDC) replenishes its stock from an outside supplier (OS), while the distribution center (DC) faces random demand and replenishes its stock from the RDC. Unlike in the traditional serial system, the DC itself can replenish its inventory from outside as well. We firstly derive structural properties for the optimal long‐run average cost and the optimal stationary policy by vanishing discount approach, and then propose two simple and easy‐to‐implement policies. The first policy, which we call the three‐index policy, combines the characteristics of the echelon‐base‐stock policy for the serial system (Clark and Scarf. 1960. Management Sci.6(4): 475–490) and the dual‐index policy for the dual‐sourcing system (Veeraraghavan and Scheller‐Wolf. 2008. Oper. Res.56(4): 850–864). We show that the order‐up‐to level of the DC from the RDC can be computed by a newsboy fractile. A simulation‐based optimization procedure for the policy is provided. We then develop the approximate linear programming (ALP) policy based on the three‐index policy and the multimodularity of the problem. This policy applies the linear programming approach to approximately solve the value function of the dynamic programming formulation. Numerical results show that both the three‐index policy and the ALP policy are comparable to the optimal policy computed via dynamic programming, and the latter performs slightly better. Moreover, the OS of the DC can draw considerable cost savings under both policies. We also conduct a numerical study with problem parameters calibrated using actual data from a consumer goods company in China to glen insights on the management of the system. [ABSTRACT FROM AUTHOR]

Robust look-ahead dispatch (RLAD) is essential to manage uncertainties in power systems. As its key step, the worst-case scenario identification (WCSI) problem is cast to a max-min program. This computationally intensive procedure has to be performed repeatedly, impairing the computational efficiency of RLAD. To address this issue, an efficient RLAD scheme incorporating critical region preparation in gap time is proposed. The computation burden is mostly transferred from the online decision stage to the gap time via a customized technique based on multi-parametric programming. The accuracy of computing the critical regions is validated by the test in a six-bus system, and the proposed method is shown to cut down 21% of total iterations and save computational time dramatically by the test in a practical-scale system in Northeastern China. [ABSTRACT FROM AUTHOR]

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of transmission and distribution price reform in China, pumped storage power station can not continue to be included in the effective assets of the power grid, and its cost can not be dredged through the transmission and distribution price, so it is urgent to find a way to protect its own income through the market. This paper innovatively proposes a "three-stage" competitive optimization model for pumped-storage power stations, using a quadratic programming algorithm with two consecutive iterations to convert the discrete programming problem into a linear convex programming problem, reducing the difficulty of calculation and improving the calculation accuracy. Finally, the reinforcement learning algorithm is used to obtain the real-time bidding strategy of the pumped storage power station, and continuous feedback is provided. The calculation example analysis shows that compared with the traditional model, the "three-stage" model can bring better benefits to the pumped storage power station, and when the actual value of demand fluctuates within −8%, the pumped storage power station has the ability to resist risks higher than the market average. And when the proportion of renewable energy increases from the current 8%–30%, the revenue of pumped storage power plants will drop by 20%. • Establish PSPS "three steps" bidding (period-sale-purchase) feedback loops optimization model. • The model roll optimization 24 h and feedback, reflect PSPS and EESM influence each other. • Taking the consideration of other competitors different bidding strategy in EESM environment. • Simulating SPSP vs other competitors gaming process, the EESM bidding scenario closer to reality. • Estimating influences of power demand and change in renewable energy capacity on PSPS earnings. [ABSTRACT FROM AUTHOR]

In train rescheduling, the arrival and departure times of trains at stations are normally constrained by many operations rules that must be respected. Therefore, different methods were proposed to describe train operations for preventing train conflicts, especially concerning the safety headways. This paper develops a virtual resource-based method in place of the commonly used big-M method and 'either-or' constraints for reformulating the safety headway of opposing trains arriving at the same station on a single-track rail. This new formulation can provide an efficient decomposition mechanism by decomposing the original complex problem into several single train-based subproblems. A resource-oriented mixed-integer linear programming model based on a time-space network is developed to minimize the total train delay time with cumulative flow variables used to describe time-space resources occupation. We also present a set of numerical experiments based on a single-track railway line in China to evaluate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

China is a large road transport country as its logistics costs account for 18% of GDP. In order to further reduce logistics costs, it is necessary to optimize the energy-saving and efficient comprehensive transportation structure in China. In this paper, we analyze the characteristics of comprehensive transportation efficiency evaluation, then based on data envelopment analysis (DEA), we select a fixed return method to scale CCR model and a variable return BCC model to establish a comprehensive transportation efficiency evaluation model to evaluate comprehensive transportation efficiency. we select comprehensive transportation efficiency evaluation indicators as a combination of the input-output evaluation indicator system. Input indicators include energy consumption, number of employees, and mileage value, and output indicators are converted turnover; and finally We build a comprehensive transportation structure optimization model based on the linear programming method, the minimum comprehensive redundant input under the transportation efficiency evaluation as the objective function, and we want to take the unit GDP transportation energy consumption intensity drop by 15% in this way; Finally, we analyze the transportation structure of Liaoning Province in China, respectively calculate the input and output index datum and carry out DEA efficiency evaluation analysis. The results show that the energy intensity of road transportation is much higher than other two modes of transportation, and the efficiency of road transportation is relatively low. [ABSTRACT FROM AUTHOR]

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios. • A drone-supported relief facility location problem is proposed. • A risk-averse distributionally robust optimisation model is established. • Three ambiguity sets are designed to describe the incomplete distribution of demand. • The equivalent computationally tractable reformulations are derived. [ABSTRACT FROM AUTHOR]

The booming e-commerce is challenging express companies by requiring higher logistics service efficiency in rural China. This study investigates a cooperative vehicle routing problem for simultaneous pickup and delivery requests, in which the delivery orders can be fulfilled either by home delivery or customer pickup. The delivery option for each order is complex due to the optional cooperative relationships among express companies, the inter-dependence between self-pickup site selection and the overall online vehicle routing scheme, and the distance-dependent compensation to self-pickup customers. The problem is formulated as a mixed integer linear programming model and solved by a tailored adaptive large neighborhood search algorithm. A cost-saving allocation mechanism based on the core guaranteed Shapley value is applied to carrier collaboration. The numerical results show that the newly introduced cooperative distribution system can significantly reduce operational costs. Such improvements are robust even if the total demand changes or under different rural logistics network topologies. • We study a CoVRPDO with simultaneous pickup and delivery. • We develop a mixed-integer linear programming model for the proposed CoVRPDO. • We propose a tailored adaptive large neighborhood search metaheuristic. • The core guaranteed Shapley value is applied to cost-saving allocation. • The results show that the proposed method achieves a significant cost saving. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are forecasted under different development scenarios. • The Moran's I was positive and increased gradually from 2010 to 2020. • The carbon emission risks vary significantly across geographical areas in Yaan. • Low-carbon optimization can reduce 24.674 × 104 t of land use carbon emissions. Land use and cover change (LUCC) is a major driver of this rapid increase in atmospheric carbon. To reasonably plan various types of land use areas and mitigate the rate of growth of carbon emissions, this study takes Yaan, Sichuan province, as a case study. First, the calculation of Yaan's land use carbon emissions (LUCE) was approached by taking land use structure into account. Subsequently, the spatiotemporal distribution of LUCE was evaluated by employing the carbon emission risk index and the Moran index. Finally, the multi-objective linear programming (MOP), the Markov chain, and the PLUS model were used to predict the spatial distribution of LUCC in 2030, including natural development scenarios (NDS) and low-carbon optimization development scenarios (LODS). According to the findings, the impervious surface is identified as the principal contributor to LUCE, while the forest is recognized as the principal absorbers of carbon. The carbon emissions in typical mountainous areas are distributed in cities and generally concentrate towards the plains in the northeast direction. Under the LODS, LUCE decrease significantly. For both NDS and LODS, the overall trend of land development direction in Yaan is "northeast-southwest" from 2020 to 2030. These results could provide some suggestions for low-carbon land use in cities like Yaan. [ABSTRACT FROM AUTHOR]

Bio-natural gas (BNG) has received recent interest as a renewable alternative to natural gas (NG). BNG supply chains (BSC), including production, transportation, and utilization, are compatible and can overlap with existing NG infrastructure. The goal is to develop a set of models that achieve the various trade-offs between economic, environmental and safety risks by combining life cycle assessment (LCA), mixed-integer linear programming (MILP) and fuzzy optimization, expanding the utilization of BSC while quantifying the risk of the whole process, so as to improve the safety awareness of the project operation. BNG technologies were considered: anaerobic digestion (AD), gasification, and syngas methanation. This model can analyze the changes in the different objectives of each module and the distribution of biomass mass flow by comparing different straw-based harvesting rates. Results indicate that AD technology possesses the lowest economic cost, and BNG as transportation fuel will generate the most benefits. The unit cost of BNG for different technologies ranges from 0.36 to 0.67 $/m³，the emission reduction is from 5.18 kg/m³ to 10.28 kg/m³，and the maximum safety risk value is 1.96. The proposed model was applied to a case study in China to illustrate the optimization of a regional straw-based BSC. The results illustrate how BNG supply chains can be systematically planned as part of an integrated decarbonization policy while mitigating safety risks that may occur from handling this fuel. [ABSTRACT FROM AUTHOR]

How to satisfy food production sustainably in the rapid urbanizing process is a vital problem for major grain-producing areas. Taking two national grain-producing regions in China, Shandong and Dongting Lake Region as examples, this study uses step-by-step prediction and a double-objective linear programming model to analyze the dilemma of land scarcity, food demand, and environmental load. It is found that food demand by 2030 will induce partial regional cropland deficiency and heterogeneous agro-environmental risk among cities. The double-objective linear programming model finds possible solutions in different scenarios: on the premise of keeping the current cropland area unchanged, the upper threshold of crop yield by 2030 will be 2539.06 × 104 t in Dongting and 7175.13 × 104 t in Shandong, respectively. On the condition of guaranteeing food self-sufficiency and minimizing the environmental load, the lower threshold of arable land area should be 99.47 × 104 ha in Dongting and 436.85 × 104 ha in Shandong. Pollutant reduction will be realized by reallocation of agro-production task and optimized practices. The food carbon impact can be reduced by up to 1.27 kg CO2 eq/kg. This study contributes to feasible pathways towards adequate agro-production, lessened land scarcity, and minimal environmental risk for rapid urbanizing cities in a sustainable way. [ABSTRACT FROM AUTHOR]

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC. [ABSTRACT FROM AUTHOR]